A method for optimizing engineering budget based on budgeted machinery requirements

Abstract

The present application discloses a method for optimizing engineering budgets based on budgeted machinery requirements. By establishing a mathematical optimization model with the goal of minimizing the total project cost, combined with heuristic algorithms and distributed computing, the scheduling and resource allocation of construction machinery are globally optimized, effectively solving the problems of low efficiency and error-prone measurement through manual resource allocation in the background technology, high machinery idle rate, insufficient multi-dimensional cost control, and lack of global optimization of the scheduling scheme. The method obtains parameters such as the budgeted machinery demand shift volume, resource curve, and job priority, and imposes multi-dimensional constraints such as the daily workload of the machinery, scheduling sequence, and entry and exit frequency to ensure that the generated scheduling scheme meets the actual construction needs. Multiple cost factors such as machinery use cost, idle cost, entry and exit cost, etc. are introduced into the optimization model to achieve the comprehensive minimization of various costs in the form of an objective function, thereby effectively reducing the total cost of the project.

Description

Technical Field

[0001] The present application relates to the technical field of engineering budget optimization, and in particular to an engineering budget optimization method based on budget machinery requirements. Background Art

[0002] As large-scale construction projects expand, the requirements for resource management and cost control during construction are also increasing. The rational allocation of construction machinery is crucial to ensuring the smooth progress of projects. In particular, within the field of Construction Cost Management Systems (CCMS), effectively allocating construction machinery resources to meet project needs has become a key concern for companies. Proper allocation of construction machinery not only ensures construction progress but also directly impacts project costs and resource efficiency.

[0003] Currently, the configuration and scheduling of construction machinery primarily relies on preliminary estimates and resource allocation based on personal experience. Construction units typically manually input and calculate resource allocation based on the demand for on-site operations and the actual conditions of the construction machinery, thereby formulating a machinery scheduling plan. However, this approach presents significant problems:

[0004] Low efficiency and prone to errors: Manual resource allocation and calculation requires a lot of time and effort, making it difficult to quickly respond to the dynamically changing needs of engineering projects. Omissions or errors are also prone to occur during the calculation process, affecting the accuracy of cost estimation.

[0005] Waste of resources: The idle rate of mechanical equipment is high, resulting in insufficient resource utilization and increasing the total cost of the project.

[0006] Insufficient scheduling optimization: Existing technologies make it difficult to comprehensively consider factors such as the machine's operating cost, scheduling cost, entry and exit costs, and job priority, resulting in a lack of global optimality in the scheduling plan and an inability to achieve effective cost control.

[0007] Therefore, in the allocation and scheduling of construction machinery resources, the low efficiency and error-prone calculation of manual resource allocation, high machinery idle rate, insufficient multi-dimensional cost control and lack of global optimization of scheduling plans have become problems that need to be solved urgently. Summary of the Invention

[0008] This application provides an engineering budget optimization method based on budget machinery demand, which aims to solve the problems in the existing technology of construction machinery resource allocation and scheduling, such as low efficiency and easy errors in manual resource allocation, high machinery idle rate, insufficient multi-dimensional cost control and lack of global optimization of scheduling schemes.

[0009] A method for optimizing engineering budget based on budgeted machinery requirements, the method comprising:

[0010] According to the operational requirements of the project, obtain the required shifts of various budgeted machines Known parameters for resource curves, resource calendars, and activity priorities;

[0011] Establish a mathematical optimization model with the goal of minimizing the total project cost, where the total cost C includes the machinery usage cost C 0 , machinery idle cost C 1 、Machinery entry and exit cost C 2 , machinery depreciation cost C 3 , construction machinery preference cost C 4 , Mechanical scheduling cost C 5 and mechanical scheduling stay penalty cost C 6 ,in:

[0012] C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ;

[0013] The presence status of construction machinery Work surface allocation status Mechanical scheduling status and the number of hours of machine replacement budget Set as decision variable;

[0014] A heuristic algorithm combined with distributed computing is used to solve the mathematical optimization model to obtain a construction machinery scheduling solution that minimizes the total cost;

[0015] Output the scheduling plan of the construction machinery, including the number of machines on site every day, the distribution of work surfaces and the number of machines to be replaced in the budget.

[0016] In the above solution, optionally, the calculation formulas for each cost item of the total cost C are as follows:

[0017] Machinery use cost:

[0018] Machinery Idle Cost:

[0019] Machinery entry and exit costs:

[0020] Machinery depreciation cost:

[0021] Construction machinery preference cost:

[0022] Scheduling costs of construction machinery between work surfaces:

[0023]

[0024] Penalty cost:

[0025] In the above solution, optionally, constraints are imposed on the mathematical optimization model, including:

[0026] Budgeted machinery demand constraints:

[0027] Daily machine workload constraints:

[0028] Mechanical entry and exit frequency constraints:

[0029] In the above solution, optionally, the constraint condition further includes:

[0030] Fixed entry and exit of construction machinery:

[0031] Construction machinery cannot enter and exit the site frequently:

[0032] Construction machinery can only be used on the work surface if it is present, and can only be used on one work surface on the same day:

[0033]

[0034] The premise that construction machine j can work on the kth operation on day t is that the construction machine happens to be on the working surface to which the kth operation belongs:

[0035]

[0036] The premise that construction machine j can replace budget machine i on day t is that construction machine j is performing the operation corresponding to budget machine i on day t:

[0037]

[0038] For the working surface K, the construction machine j can be transferred out only if no construction machine of the same type as the construction machine j is transferred in within the specified time:

[0039]

[0040] For operation k, if no construction machine of the same type as construction machine j is transferred in the next day, construction machine j can be transferred out of operation k.

[0041]

[0042] in, Is the jth construction machine present on day t? Is the jth construction machine on the tth day in the A working surface, Is the jth construction machine working on the kth operation on the tth day? The number of shifts required to replace the budgeted machinery of type i for the j-th construction machine on day t is: Whether the jth construction machine enters the site on the tth day, is the working surface index, indicating the A working surface, Index of construction machinery types, indicating Class construction machinery, k is the operation index, indicating the k-th operation, i is the budget machinery index, indicating the i-th class budget machinery, j is the construction machinery index, indicating the j-th construction machinery, t is the construction period index, indicating the construction period of the t-th day, Is the kth job part of the Working surface, 1 means it belongs to, 0 means it does not belong to, Is the budget machine of type i belonging to the kth job, 1 means it belongs, 0 means it does not belong, Is the jth construction machine part of the Class construction machinery, 1 means it belongs to, 0 means it does not belong to, is the unit-shift usage cost of the j-th construction machine, is the idle cost per unit shift of the j-th construction machine, is the single entry and exit cost of the j-th construction machine, is the depreciation cost of the j-th construction machine per day, The unit incentive cost of replacing the i-th budgeted machine with the j-th construction machine, For the jth construction machine from the The work surface is dispatched to the The scheduling cost of each work surface, is the penalty cost of the jth construction machine not being on any working surface, P ij is the preference of the j-th construction machine to the i-th budget machine, V j is the remaining depreciation period of the j-th construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day. ij is the efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine, is the maximum working shift of the j-th construction machine on the t-th day after correction, Estimate the required number of working hours for the i-th type of machinery on day t.

[0043] In the above solution, optionally, the heuristic algorithm includes the following steps:

[0044] Generate an initial solution based on resource requirements and constraints;

[0045] The initial solution is optimized by genetic algorithm, which includes population initialization, selection, crossover and mutation operations;

[0046] The solution is evaluated based on the fitness function, which is a total cost formula;

[0047] After multiple iterations, the optimal solution is output.

[0048] In the above solution, optionally, the distributed computing includes the following steps:

[0049] Decompose the optimization problem into multiple sub-problems and distribute them to different computing nodes;

[0050] Each computing node calculates the local optimal solution of the subproblem in parallel;

[0051] Summarize and merge all local optimal solutions to generate an approximate global optimal solution;

[0052] The approximate global optimal solution is further optimized and the final result is output.

[0053] In the above solution, optionally, the resource curve is used to describe the daily usage requirements of various budgeted machines for each operation, and the resource calendar is used to define the maximum daily working hours of the construction machinery.

[0054] In the above solution, optionally, the job priority is used to determine the scheduling order of construction machinery between different work surfaces, and jobs with higher priorities are given priority in resource allocation.

[0055] In the above solution, optionally, the scheduling time constraints between the work surfaces include:

[0056] Construction machinery from the The work surface is dispatched to the Time constraints required for each work surface:

[0057]

[0058] In the above solution, optionally, the output scheduling plan includes the presence status, operation allocation and scheduling path of each type of construction machinery on each day.

[0059] Compared with the prior art, this application has at least the following beneficial effects:

[0060] Based on further analysis and research of existing technical issues, this application recognizes that existing construction machinery resource allocation and scheduling methods suffer from low efficiency and error-prone manual resource allocation calculations, high machine idle rates, insufficient multi-dimensional cost control, and a lack of global optimization in scheduling solutions. By establishing a mathematical optimization model with the goal of minimizing total project costs, combined with heuristic algorithms and distributed computing, this method globally optimizes the scheduling and resource allocation of construction machinery, effectively addressing these issues in the prior art: low efficiency and error-prone manual resource allocation calculations, high machine idle rates, insufficient multi-dimensional cost control, and a lack of global optimization in scheduling solutions. This method obtains parameters such as the budgeted machine demand, resource curves, and job priorities, and applies multi-dimensional constraints such as daily machine workload, scheduling sequence, and entry and exit frequency to ensure that the generated scheduling solution meets actual construction needs. Multiple cost factors, such as machine usage cost, idle cost, and entry and exit costs, are introduced into the optimization model to achieve comprehensive minimization of these costs in the form of an objective function, significantly reducing total project expenditure. Furthermore, a heuristic algorithm is used to quickly generate initial solutions, and distributed computing is used to improve solution efficiency, enabling large-scale scheduling problems to be solved within a limited timeframe. The final scheduling plan can not only dynamically adapt to changes in project needs, but also avoid excessive idleness and frequent entry and exit of machinery, improve machinery utilization, and significantly improve scheduling efficiency and economy, thereby fully meeting the efficient and low-cost resource management requirements of modern projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A flowchart of an engineering budget optimization method based on budgeted machinery requirements is provided in accordance with an embodiment of the present application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] In one embodiment, Figure 1 As shown, a method for optimizing engineering budget based on budgeted machinery requirements is provided, comprising the following steps:

[0064] According to the operational requirements of the project, obtain the required shift quantity of various budgeted machines D i t , resource curves, resource calendars, and activity priority known parameters;

[0065] Establish a mathematical optimization model with the goal of minimizing the total project cost, where the total cost C includes the machinery usage cost C 0 , machinery idle cost C 1 、Machinery entry and exit cost C2 , machinery depreciation cost C 3 , construction machinery preference cost C 4 , Mechanical scheduling cost C 5 and mechanical scheduling stay penalty cost C 6 ,in:

[0066] C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ;

[0067] The presence status of construction machinery Work surface allocation status Mechanical scheduling status and the number of hours of machine replacement budget Set as decision variable;

[0068] A heuristic algorithm combined with distributed computing is used to solve the mathematical optimization model to obtain a construction machinery scheduling plan that minimizes the total cost;

[0069] Output the scheduling plan of the construction machinery, including the number of machines on site every day, the distribution of work surfaces and the number of machines to be replaced in the budget.

[0070] In this embodiment, the calculation formulas for each cost item of the total cost C are as follows:

[0071] Machinery use cost:

[0072] Machinery Idle Cost:

[0073] Machinery entry and exit costs:

[0074] Machinery depreciation cost:

[0075] Construction machinery preference cost:

[0076] Scheduling costs of construction machinery between work surfaces:

[0077]

[0078] Penalty cost:

[0079] In this embodiment, constraints are imposed on the mathematical optimization model, including:

[0080] Budgeted machinery demand constraints:

[0081] Daily machine workload constraints:

[0082] Mechanical entry and exit frequency constraints:

[0083] In this embodiment, the constraints also include:

[0084] Fixed entry and exit of construction machinery:

[0085] Construction machinery cannot enter and exit the site frequently:

[0086] Construction machinery can only be used on the work surface if it is present, and can only be used on one work surface on the same day:

[0087]

[0088] The premise that construction machine j can work on the kth operation on day t is that the construction machine happens to be on the working surface to which the kth operation belongs:

[0089]

[0090] The premise that construction machine j can replace budget machine i on day t is that construction machine j is performing the operation corresponding to budget machine i on day t:

[0091]

[0092] For the working surface K, the construction machine j can be transferred out only if no construction machine of the same type as the construction machine j is transferred in within the specified time:

[0093]

[0094] For operation k, construction machine j can be transferred out of operation k only if no construction machine of the same type as construction machine j is transferred in the next day.

[0095]

[0096] in, Is the jth construction machine present on day t? Is the jth construction machine on the tth day in the A working surface, Is the jth construction machine working on the kth operation on the tth day? The number of shifts required to replace the budgeted machinery of type i for the j-th construction machine on day t is: Whether the jth construction machine enters the site on the tth day, is the working surface index, indicating the A working surface, Index of construction machinery types, indicating Class construction machinery, k is the operation index, indicating the k-th operation, i is the budget machinery index, indicating the i-th class budget machinery, j is the construction machinery index, indicating the j-th construction machinery, t is the construction period index, indicating the construction period of the t-th day, Is the kth job part of the Working surface, 1 means it belongs to, 0 means it does not belong to, Is the budget machine of type i belonging to the kth job, 1 means it belongs, 0 means it does not belong, Is the jth construction machine part of the Class construction machinery, 1 means it belongs to, 0 means it does not belong to, is the unit-shift usage cost of the j-th construction machine, is the idle cost per unit shift of the j-th construction machine, is the single entry and exit cost of the j-th construction machine, is the depreciation cost of the j-th construction machine per day, The unit incentive cost of replacing the i-th budgeted machine with the j-th construction machine, For the jth construction machine from the The work surface is dispatched to the The scheduling cost of each work surface, is the penalty cost of the jth construction machine not being on any working surface, P ij is the preference of the j-th construction machine to the i-th budget machine, V j is the remaining depreciation period of the j-th construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day. ij is the efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine, is the maximum working shift of the j-th construction machine on the t-th day after correction, Estimate the required number of working hours for the i-th type of machinery on day t.

[0097] In this embodiment, the heuristic algorithm includes the following steps:

[0098] Generate an initial solution based on resource requirements and constraints;

[0099] The initial solution is optimized by genetic algorithm, which includes population initialization, selection, crossover and mutation operations;

[0100] The solution is evaluated based on the fitness function, which is a total cost formula;

[0101] After multiple iterations, the optimal solution is output.

[0102] In this embodiment, the distributed computing includes the following steps:

[0103] Decompose the optimization problem into multiple sub-problems and distribute them to different computing nodes;

[0104] Each computing node calculates the local optimal solution of the subproblem in parallel;

[0105] Summarize and merge all local optimal solutions to generate an approximate global optimal solution;

[0106] The approximate global optimal solution is further optimized and the final result is output.

[0107] In this embodiment, the resource curve is used to describe the daily usage requirements of various budgeted machines for each operation, and the resource calendar is used to define the maximum daily working hours of the construction machines.

[0108] In this embodiment, the job priority is used to determine the scheduling order of construction machinery among different work surfaces, and jobs with higher priorities are given priority in resource allocation.

[0109] In this embodiment, the scheduling time constraints between the work surfaces include:

[0110] Construction machinery from the The work surface is dispatched to the Time constraints required for each work surface:

[0111]

[0112] In this embodiment, the output scheduling plan includes the presence status, operation allocation and scheduling path of each type of construction machinery on each day.

[0113] The above specific implementation steps can significantly solve the problems of construction machinery resource allocation and scheduling mentioned in the background technology:

[0114] Through heuristic algorithms and distributed computing, it replaces the traditional scheduling method that relies on manual resource configuration and measurement, greatly improving computing efficiency and enabling rapid response to dynamically changing needs in engineering projects.

[0115] The optimization model controls the daily workload and replacement ratio of machinery through constraints to avoid excessive idleness of machinery, thereby improving resource utilization efficiency.

[0116] Multi-dimensional cost items (usage cost, idle cost, entry and exit cost, depreciation cost, etc.) are introduced into the model, and the total cost of the project is minimized through the optimization of the objective function, effectively reducing project expenses.

[0117] By comprehensively considering budgetary machinery requirements, job priorities, time windows, and other conditions, we ensure that the optimized plan meets actual construction requirements and improves the feasibility and applicability of the plan.

[0118] Genetic algorithms combined with distributed computing can generate scheduling solutions that are close to the global optimal solution within a limited time, avoiding the problem of local optimality in traditional methods and thus significantly improving scheduling quality.

[0119] This embodiment can effectively solve the problems mentioned in the background technology, such as low efficiency and prone to errors in manual resource configuration, high machinery idle rate, insufficient multi-dimensional cost control, and lack of global optimization of scheduling schemes, and comprehensively improve the economy and efficiency of construction machinery resource configuration and scheduling.

[0120] In one implementation, an algorithm-based coordinated scheduling of construction machinery reduces idle equipment and ineffective scheduling, maximizes resource utilization, and reduces construction costs for the enterprise. By using the algorithm's constraints to meet the project's budgeted machinery requirements, resources are optimally allocated while minimizing project costs, providing cost-optimized configuration references for machinery dispatchers.

[0121] Through dynamic cost accounting models and automated scheduling optimization solutions, we can achieve comprehensive minimization of multi-dimensional costs such as construction machinery usage costs, scheduling costs, entry and exit costs, effectively reducing total project expenditures and improving project profit margins.

[0122] By combining heuristic algorithms with distributed computing frameworks, feasible and optimal solutions can be quickly found, enabling large-scale scheduling problems to be calculated and optimized within a limited time, providing immediate support for the formulation and adjustment of construction plans.

[0123] This embodiment introduces a multi-objective optimization mechanism into the construction cost management system, balancing total cost minimization with optimal scheduling time to meet the different priorities in real-world business scenarios. The solution for replacing budgeted machinery is optimized to minimize the total project cost while meeting the budgeted machinery requirements.

[0124] Based on model requirements and customer-provided data, this implementation aims to minimize costs, using known customer-provided parameters and various requirements as constraints. A mathematical algorithm model is designed and constructed. The optimal feasible solution for the algorithm is then determined, yielding: the number of construction machines of a certain type on-site on a given day, the total number of shifts of a certain type of construction machine that can replace a certain budgeted type of machine on that day, and the total project cost under the current solution. The overall solution, based on the resource curve requirements of the operation, outputs a scheduling plan for machine resources across different work surfaces, minimizing costs.

[0125] The core mathematical model of this embodiment is divided into the following parts:

[0126] Objective function: defines the goal to be optimized by the model, that is, outputting the resource name, configuration quantity and status (i.e. scheduling requirements) of each job in each duration to make the total cost more economical, and outputting the total cost.

[0127] Decision variables: represent the variable quantities in the model, namely the number of construction machines on site and the total number of machines to be replaced in the budget.

[0128] Constraints: Ensure that the solution meets various customer needs and known parameters, such as the unit machine usage cost, resource curve and construction period requirements, construction machinery maintaining continuous operation, construction machinery cannot be dispatched during the continuous working period, construction machinery needs to meet the job priority during scheduling, the scheduling cost of construction machinery between work surfaces, and the idle cost of machinery resources.

[0129] Known parameters: Provide the specific values ​​required by the model, such as resource curves, resource calendars, all resources under the job, daily scheduling costs, job scheduling costs, job scheduling time, job priority, and fixed entry and exit times.

[0130] Solution and Result: Minimum overall cost. How to schedule these machines to meet the scheduling requirements between operations, satisfy the resource curve requirements of each operation, and simultaneously meet the cost between the two operations, thus completing the project task at the lowest possible cost.

[0131] Overall implementation logic and steps:

[0132] Mathematical model construction and analysis:

[0133] The mathematical model for this project is essentially a multi-constrained resource optimization problem in a distributed computing environment, a large-scale optimization problem within the disciplines of operations research and management science. By combining heuristic algorithms with distributed solving, an approximate solution is obtained. Based on this approximate solution, a global optimization solution is then applied using heuristic algorithms. Specifically, while ensuring that constraints such as resource requirements and scheduling time are met, the model dynamically adjusts the objective function weights to find the optimal solution between reducing costs and improving resource utilization. This allows the most economical configuration to be selected, minimizing the total cost of the project.

[0134] Known conditions:

[0135] The budgeted machinery required for each operation, the resource curve requirements for each operation, and the working time for each operation.

[0136] Resource curve: The daily usage of each budgeted machine for each operation.

[0137] Resource Calendar: The maximum working hours of a resource on a certain day.

[0138] All resources under the operation: All construction machines under each operation.

[0139] Daily dispatching cost: daily dispatching cost of each type of construction machinery, including daily dispatching hours and daily dispatching cost.

[0140] Operation scheduling cost: the scheduling cost of each type of construction machinery between different operation surfaces.

[0141] Operation scheduling time: the scheduling time of each type of construction machinery between different operation surfaces.

[0142] Job priority: The scheduling priority of each type of construction machinery between different jobs.

[0143] Fixed entry and exit times: entry and exit times for some construction machinery.

[0144] Development and solution:

[0145] Objective Function: Based on known parameters, calculate the resource name, configuration quantity, and status (i.e., scheduling requirements) for each job in each duration under the current plan to make the total cost more economical, and output the total cost.

[0146] Decision variables:

[0147] Whether the jth construction machine is present on day t (0-1);

[0148] Is the jth construction machine on the tth day Working surface (0-1);

[0149] Whether the jth construction machine is working on the kth operation on the tth day (0-1);

[0150] The number of shifts of the j-th construction machine that replaces the i-th budgeted machine demand on day t (continuous variable);

[0151] Whether the jth construction machine enters the site on day t (0-1 variable);

[0152]

Known parameters

[0153] Index and corresponding relationship:

[0154] Working surface index, indicating the A working surface;

[0155] Construction machinery type index, indicating the Construction machinery;

[0156] k: job index, indicating the kth job;

[0157] i: budget machine index, indicating the i-th category of budget machine;

[0158] j: construction machine index, indicating the jth construction machine;

[0159] t: duration index, indicating the duration of the tth day;

[0160] Does the kth job belong to the Working surface, 1 means it belongs to, 0 means it does not belong to;

[0161] Whether the i-th budget machine belongs to the k-th job, 1 means it does, 0 means it does not; Does the jth construction machine belong to Class: construction machinery, 1 means it belongs to, 0 means it does not belong to;

[0162] Unit cost parameters:

[0163] The unit-shift usage cost of the j-th construction machine;

[0164] Idle cost per unit shift of the j-th construction machine;

[0165] The single entry and exit cost of the j-th construction machine (including entry and exit costs);

[0166] The depreciation cost of the j-th construction machine per day;

[0167] Unit incentive cost of replacing budgeted machinery of category i with the j-th construction machine (algorithm built-in parameter, the value is a small negative number) The jth construction machine from The work surface is dispatched to the The scheduling cost of each work surface;

[0168] The penalty cost for the j-th construction machine not being on any working surface (a built-in parameter of the algorithm, with a large positive value);

[0169] P ij : The preference degree of the jth construction machine to the i-th budget machine (the more preference, the higher the value);

[0170] V j : The remaining depreciation period of the j-th construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day;

[0171] Requirements related parameters:

[0172] B ij : The efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine;

[0173] The corrected maximum work shift of the j-th construction machine on day t;

[0174] The required number of shifts for the i-th type of budgeted machinery on day t;

[0175] Other parameters:

[0176] B ij : The efficiency conversion coefficient when the i-th budget machine is replaced by the j-th construction machine;

[0177] The corrected maximum work shift of the j-th construction machine on day t;

[0178] The required number of shifts for the i-th type of budgeted machinery on day t;

[0179]

Model construction

[0180] Objective function: Total cost C = construction machinery use cost C 0 + Construction machinery idle cost C 1 +Construction machinery entry and exit costs C 2 + Construction machinery depreciation cost C 3 + Construction machinery preference cost C 4 +Construction machinery operation surface scheduling cost C 5 + Penalty cost C for construction machinery “staying” between work surfaces 6 ;

[0181] C=C 0 +C 1 +C 2 +C 3 +C 4 +C 5 +C 6 ;

[0182] Cost of using construction machinery:

[0183]

[0184] Idle cost of construction machinery. When the number of replacement shifts exceeds one shift, there is no idle cost (max will be linearized during the solution);

[0185]

[0186] Cost of construction machinery entering and leaving the site;

[0187]

[0188] Depreciation cost of construction machinery (min will be linearized during actual solution);

[0189]

[0190] construction machinery preference cost (virtual cost of model control soft constraints);

[0191]

[0192] The cost of dispatching construction machinery between work surfaces (variables will be multiplied and linearized during solution);

[0193]

[0194] Penalty costs (ensuring that construction machinery does not "linger" when dispatching between work surfaces and is dispatched as quickly as possible to ensure the correct expression of dispatch costs);

[0195]

[0196] Constraints:

[0197] Construction machinery needs to meet the needs of budget machinery;

[0198]

[0199] When construction machinery replaces budgeted machinery, the daily work shifts cannot be exceeded;

[0200]

[0201] Construction machinery with a conversion coefficient of 0 cannot replace budgeted machinery;

[0202]

[0203] Fixed entry and exit points for construction machinery (only restrictions are imposed on construction machinery with fixed entry and exit points);

[0204]

[0205] Construction machinery cannot enter and exit the site frequently (it must wait 6 months after exiting the site before it can enter the site again);

[0206]

[0207] Construction machinery from the The work surface is dispatched to the The time constraints required for each operation surface;

[0208]

[0209] Construction machinery can only be on the working surface if it is present, and can only be on one working surface on the same day;

[0210]

[0211] The premise for construction machine j to work on the kth operation on day t is that the construction machine happens to be on the kth operation

[0212] The work surface to which the work belongs;

[0213]

[0214] The premise that construction machine j can replace budget machine i on day t is if and only if construction machine j on day t

[0215] The corresponding operation work of machine i is being budgeted;

[0216]

[0217] To avoid frequent scheduling, the idle time of construction machinery must be greater than (the time of scheduling on the working surface + 1 day) before scheduling can be carried out, and there must be no "adjustment" between working surfaces. Therefore, the constraint is equivalent to: for the working surface If no construction machinery of the same type as construction machinery j is transferred in within the specified time, construction machinery j can be removed from the working area. Call out.

[0218]

[0219] A construction machine can only be scheduled for different operations on the same work surface if its idle time is greater than one day. Therefore, the constraint equivalently translates to: for operation k, construction machine j can only be dispatched from operation k if no construction machine of the same type as construction machine j is dispatched the next day.

[0220]

[0221] The innovation of this embodiment lies in proposing a construction machinery resource scheduling optimization method based on a distributed computing environment. Combined with a heuristic algorithm, it solves the complexity and real-time problems of large-scale construction machinery scheduling problems by generating initial solutions in a distributed manner and performing global optimization iterations. The core lies in organically combining multi-dimensional factors such as the job demand resource curve, machinery scheduling costs, and time constraints and priority constraints between work surfaces to construct a multi-constraint, multi-objective optimization model. Through innovative algorithm design, it achieves rapid output of scheduling solutions and minimization of total costs in massive data scenarios. This method breaks through the limitations of traditional linear programming that is difficult to efficiently solve complex nonlinear constraints, and has the significant characteristics of high computational efficiency, strong economic scheduling solutions, and high practical applicability.

[0222] The technical effects brought about by adopting the technical solution of this embodiment are as follows:

[0223] Improve computing efficiency: Through the combination of distributed computing and heuristic algorithms, rapid generation and optimization of mechanical scheduling plans can be achieved in large-scale operation scenarios, outputting high-quality scheduling results to meet real-time requirements.

[0224] Effectively reduce total project costs: By considering usage costs, idle costs, depreciation costs, entry and exit costs, and newly added scheduling costs, a more realistic cost model is constructed to achieve the goal of minimizing total costs and effectively reduce total project costs.

[0225] Optimize resource utilization: Based on constraints such as job demand resource curves and scheduling priorities, scientifically allocate machinery and equipment to reduce resource waste and improve machinery utilization, while avoiding inefficient behaviors such as frequent entry and exit.

[0226] Improve the applicability of scheduling plans: By comprehensively considering actual construction conditions such as construction needs, work surface scheduling sequence, time windows and priorities, the generated scheduling plans have higher operability and flexibility to meet on-site management needs.

[0227] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for optimizing engineering budget based on budgeted machinery requirements, characterized in that: The following steps are involved: According to the operational requirements of the project, obtain the required shifts of various budgeted machines , resource curves, resource calendars, and activity priority known parameters; Establish a mathematical optimization model with the goal of minimizing the total project cost. The total cost C includes the cost of machinery use. , machinery idle cost , Machinery entry and exit costs , machinery depreciation cost , construction machinery preference cost , machinery scheduling costs and mechanical dispatch stay penalty costs ,in: ； The presence status of construction machinery , work surface allocation status , Mechanical scheduling status and the number of hours of machine replacement budget Set as decision variable; A heuristic algorithm combined with distributed computing is used to solve the mathematical optimization model to obtain a construction machinery scheduling plan that minimizes the total cost; Output the scheduling plan for the construction machinery, including the number of machines on site each day, the allocation of work surfaces, and the number of shifts of replacement budgeted machines; The total cost The calculation formulas for each cost item are as follows: Machinery use cost: ; Machinery Idle Cost: ; Machinery entry and exit costs: ; Machinery depreciation cost: ; Construction machinery preference cost: ; Scheduling costs of construction machinery between work surfaces: ; Penalty cost: ; Constraints are imposed on the mathematical optimization model, including: The budgeted machinery demand constraint, day construction machinery combined efficiency conversion coefficient Replace The total number of work shifts for the budgeted machines of the i-th category must be consistent with the required number of work shifts for the budgeted machines of the i-th category on day t: ; Budget the required number of shifts for the i-th type of machinery on day t; The daily workload constraint of the machine, that is, the work shift of the j-th construction machine on the t-th day cannot exceed the maximum workload of the construction machine on that day: ; is the maximum work shift of the j-th construction machine on the t-th day after correction; The budget machine type constraint for machine replacement is that the j-th construction machine cannot replace the budget machine with a conversion coefficient of 0 for the construction machine: ;in, is the total cost of using construction machinery, is the unit-shift usage cost of the j-th construction machine, The number of shifts required to replace the budgeted machinery of type i for the j-th construction machine on day t; is the total idle cost of construction machinery, is the idle cost per unit shift of the j-th construction machine, Is the jth construction machine present on day t, 1 means present, 0 means absent, Indicates that idle cost will only be incurred when the corresponding construction machinery is present but the total working time is less than one shift; is the total dispatching cost of construction machinery entering and leaving the site, is the single entry and exit cost of the j-th construction machine, Whether the j-th construction machine entered the site on day t, 1 means it entered the site, and 0 means it did not enter the site; is the total depreciation cost of construction machinery, is the depreciation cost of the j-th construction machine per day, is the remaining depreciation period of the j-th construction machine. For construction machines with fixed depreciation costs, the remaining depreciation period is fixed at 1 day. Indicates that the maximum depreciation days for the corresponding construction machinery does not exceed its remaining depreciation period; is the total preference selection cost of construction machinery, which is one of the penalty costs of the algorithm. The unit incentive cost of replacing the i-th budgeted machine with the j-th construction machine, is a small negative value, is the preference degree of the jth construction machine to the i-th budget machine, and the larger the value, the higher the preference degree; is the total inter-operation-surface scheduling cost of construction machinery, For the jth construction machine from the The work surface is dispatched to the The scheduling cost of each work surface, Is the jth construction machine on the tth day in the Working surface, 1 means it is on the corresponding working surface, 0 means it is not on the corresponding working surface, For the The work surface is dispatched to the The scheduling time required for each work surface, Indicates whether the jth construction machine starts from the tth day The work surface is dispatched to the A working surface; is the penalty cost of the total construction machinery being present but not on any working surface, which is one of the penalty costs of the algorithm. is the penalty cost of the j-th construction machine not being on any working surface; The heuristic algorithm includes the following steps: Generate an initial solution based on resource requirements and constraints; The initial solution is optimized by genetic algorithm, which includes population initialization, selection, crossover and mutation operations; The solution is evaluated based on the fitness function, which is a total cost formula; Output the optimal solution after multiple iterations; The resource curve is used to describe the daily usage requirements of various budgeted machines for each operation, and the resource calendar is used to define the maximum daily working hours of construction machines.

2. The engineering budget optimization method according to claim 1, characterized in that: The constraints also include: Fixed entry and exit of construction machinery: ; Construction machinery cannot enter and exit the site frequently: ; Construction machinery can only be used on the work surface if it is present, and can only be used on one work surface on the same day: ； The premise that construction machine j can work on the kth operation on day t is that the construction machine happens to be on the working surface to which the kth operation belongs: ； The premise that construction machine j can replace budget machine i on day t is that construction machine j is performing the operation corresponding to budget machine i on day t: ； For the working surface If no construction machinery of the same type as construction machinery j is transferred in within the specified time, construction machinery j can be removed from the working area. Call out: For operation k, if no construction machine of the same type as construction machine j is transferred in the next day, construction machine j can be transferred out of operation k. ;in, For the given j-th construction machine on the t-th day, for the construction machine without fixed entry and exit, no fixed entry and exit constraints are added. Is the jth construction machine on the tth day in the A working surface, Is the jth construction machine working on the kth operation on the tth day? The number of shifts required to replace the budgeted machinery of type i for the j-th construction machine on day t is: Whether the jth construction machine enters the site on the tth day, is the working surface index, indicating the A working surface, Index of construction machinery types, indicating Class construction machinery, k is the operation index, indicating the k-th operation, i is the budget machinery index, indicating the i-th class budget machinery, j is the construction machinery index, indicating the j-th construction machinery, t is the construction period index, indicating the construction period of the t-th day, Is the kth job part of the Working surface, 1 means it belongs to, 0 means it does not belong to, Is the budgeted machinery for category i classified as Jobs, 1 means it belongs to, 0 means it does not belong to, Is the jth construction machine part of the Class construction machinery, 1 means it belongs to, 0 means it does not belong to, is a very large number, For the The work surface is dispatched to the The scheduling time required for each work surface.

3. The engineering budget optimization method according to claim 1, characterized in that: The distributed computing includes the following steps: Decompose the optimization problem into multiple sub-problems and distribute them to different computing nodes; Each computing node calculates the local optimal solution of the subproblem in parallel; Summarize and merge all local optimal solutions to generate an approximate global optimal solution; The approximate global optimal solution is further optimized and the final result is output.

4. The engineering budget optimization method according to claim 1, characterized in that: The job priority is used to determine the scheduling order of construction machinery among different work surfaces, and jobs with higher priorities are given priority in resource allocation.

5. The engineering budget optimization method according to claim 1, characterized in that: The scheduling time constraints between the work surfaces include: Construction machinery from the The work surface is dispatched to the Time constraints required for each work surface: 。 6. The engineering budget optimization method according to claim 1, characterized in that: The output scheduling plan includes the presence status, job allocation and scheduling path of each type of construction machinery on each day.

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